進階駕駛輔助系統(Advanced Driver Assist Systems, ADAS)之目的是提供車輛周圍環境與行車狀況相關資訊給駕駛人，增加行車的安全。在高速公路上駕駛人比較關心車道偏移與前車碰撞的狀況發生，因此本論文提出的進階駕駛輔助系統著重在車道追蹤、偏移和前車偵測，並且透過影像處理技術實現。本論文主要提供以下資訊：第一個是車道偵測，第二個是車道追蹤，第三個是前車偵測。在車道偵測，我們結合車道線濾波與邊緣角度分布函數來濾除大部分非車道線的雜訊，使得車道線可以被準確地偵測；在車道追蹤，利用行車影像的連續特性，預測下一張影像的感興趣車道線區域，並在感興趣車道線區域內利用霍氏轉換法追蹤車道線，除此之外無論遇到變換車道或出口匝道，提出的方法依舊能夠準確地追蹤車道；在前車偵測，根據車道內前方車輛後方的底邊、垂直邊緣、對稱性和影像密度等特徵進行偵測。 本論文提出之系統於640×360解析度的影像品質下車道偵測與追蹤的平均正確率達到97.00% ，平均執行時間為5.4ms；而前方車輛偵測的平均正確率達到84.86% ，平均執行時間為2ms。

Advanced driver assist systems (ADAS) are technologies that provide the driver with essential information when driving and lead to an increase in safe driving. On a long stretch of highway, the lane departure and forward vehicle collision are very important issues for drivers. The proposed ADAS focuses on implementing lane tracking, lane departure detection and forward vehicle detection by using the image processing techniques. In this thesis, our algorithms can provide the following basic information: (1) Lane detection, (2) Lane tracking, (3) Forward vehicle detection. In lane detection, our algorithms first combine the edge distribution function (EDF) with lane marking filter to improve the effects of non-lane marking noise and then detect the lane marking correctly. In lane tracking, the proposed method can predict the region of interest (ROI) of lane marking by the properties of continuous video in the next frame. Then, ROI is examined to track lane marking by Hough transform. In addition, no matter whether a vehicle is changing lane or passing exit-ramp, our algorithms can keep track of the lane marking smoothing. In forward vehicle detection, the position of the forward vehicle inside its own lane is determined by the features of rear shapes of vehicle, such as shadow, horizontal edge, vertical edge, symmetry and image density. Through video streams with resolution of 640×360 pixels, the experimental results achieve the average detection rate of lane tracking at 97.00% with the average processing time 5.4 ms per frame and the average detection rate of forward vehicle at 84.86% with average processing time 2 ms per frame.

[1] A. Saudi, J. Teo, M. H. A. Hijazi, and J. Sulaiman, “Fast Lane Detectionwith Randomized Hough Transform,” Information Technology, vol. 4, pp. 1–5, 2008.
[2] A. Cortes, O. Otaegui, J. Arrospide, and L. Salgado, “Real-Time Lane Tracking Using Rao-Blackwellized Particle Filter,” Journal of Real-Time Image Processing, pp. 1-13, 2012.
[3] J. Lee, “A Machine Vision System for Lane-Departure Detection,” Computer Vision and Image Understanding, vol. 86, pp. 52–78, 2002.
[4] C. R. Jung, C.R. Kelber, “Robust Linear-Parabolic Model for Lane Following,” Computer Graphics and Image Processing, pp. 72–79, 2004.
[5] N. D. Marcos, “Detection and Tracking of Vanishing Points in Dynamic Environments,” E.T.S.I. Telecommunication (UPM), 2010.
[6] N. D. Marcos, “Road Environment Modeling Using Robust Perspective Analysis,” Machine Vision and Applications, vol.22, pp. 927-945, 2011
[7] R. Hartley, A. Zisserman, “Multiple View Geometry in Computer Vision,” Cambridge University Press, 2003.
[8] S. S. Huang , “On-Board Vision System for Lane Recognition and Front-Vehicle Detection to Enhance Driver's Awareness,” Robotics and Automation, vol.3, pp. 2456 – 2461, 2004.
[9] D. C. Tseng, “Monocular Computer Vision Aided Road Vehicle Driving for Safety,” U.S. Patent No. 6765480, 2004.
[10] 紀文亮，“利用車道和汽車追蹤之智慧型CCD影像駕駛輔助系統”，國立成功大學資訊工程所碩士論文，民國九十五年。
[11] 蕭瑞聖，「先進駕駛輔助系統產品應用與機會」，工研院IEK，民國一○二年，出處：http://www2.itis.org.tw。
[12] 陳育菘，「雙視覺前方安全系統」，財團法人車輛研究測試中心，民國一○○年。
[13] 謝育靜，「2013年台灣汽車市場回顧」，財團法人車輛中心，民國一○三年，出處：http://www.artc.org.tw/chinese/03_service/03_02detail.aspx?pid=2565。
[14] 林豐博，曾平毅，林國顯，蘇振維，張瓊文，鄭嘉盈，呂怡青，劉國慶，陳昭堯，王怡方，「2011 年臺灣公路容量手冊」，交通部運輸研究所，民國一○○年。
[15] 鍾國亮，「影像處理與電腦視覺，4th ed」，台灣東華書局股份有限公司，民國一○○年。